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Momentum in R: Part 2

Many of the sites I linked to in the previous post have articles or papers on momentum investing that investigate the typical ranking factors; 3, 6, 9, and 12 month returns. Most (not all) of the articles seek to find which is the “best” look-back period to rank the assets. Say that the outcome of the article is that the 6 month look-back has the highest returns. A trading a strategy that just uses a 6 month look-back period to rank the assets leaves me vulnerable to over-fitting based on the backtest results. The backtest tells us nothing more than which strategy performed the best in the past, it tells us nothing about the future… duh!

Whenever I review the results from backtests, I always ask myself a lot of “what if” questions. Here are 3 “what if” questions that I would ask for this backtest are:

What if the strategy based on a 6 month look-back under performs and the 9 month or 3 month starts to over perform?

What if the strategies based on 3, 6, and 9 month look-back periods have about the same return and risk profile, which strategy should I trade?

What if the assets with high volatility are dominating the rankings and hence driving the returns?

The backtests shown are simple backtests meant to demonstrate the variability in returns based on look-back periods and number of assets traded.

The graphs below show the performance of a momentum strategy using 3, 6, 9, and 12 month returns and trading the Top 1, 4, and 8 ranked assets. You will notice that there is significant volatility and variability in returns only trading 1 asset. The variability between look-back periods is reduced, but there is still no one clear “best” look-back period. There are periods of under performance and over performance for all look back periods in the test.

rbresearch

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Here is the R code used for the backtests and the plots. Leave a comment if you have any questions about the code below.